.. _end_user_manual:

End User Manual
================

Detailed instructions for configuring and operating every component of Attribution Analysis. Use
this section as your in-depth reference once you have completed the :ref:`quick_start` and need to
lock in the specifics of your data, your parameters, and your execution workflow.

If you are looking for ready-made recipes, skip ahead to :ref:`use_cases`. Otherwise, work through
this manual in order: start with **Data Formats** to understand what the pipeline expects, then
**Configuration Reference** to map your data to the package's vocabulary, then choose one of the
two execution workflows below.

Choose your workflow
---------------------

Attribution Analysis supports two execution paths. Pick the one that matches how your team
operates today; you can always switch later because both consume the same data and produce the
same output.

.. list-table::
   :widths: 25 75
   :header-rows: 1

   * - Workflow
     - Best fit when…
   * - :ref:`excel_workflow`
     - Parameters are tuned by analysts who edit ``Config/attribution_analysis_parameters.xlsx``
       directly. No Python needed; the run is launched from a terminal with
       ``kaxanuk.attribution_analysis run``.
   * - :ref:`programmatic_usage`
     - You need to script variations, run batches of portfolios, or integrate the pipeline into a
       larger Python workflow (CI, Jupyter notebooks, dashboards). The Excel workbook is still
       used for configuration, but the run is driven through ``ExcelConfigurator`` and ``main()``.

What you'll find here:

- :ref:`data_formats` — the market data, portfolio, benchmark, and factor return file formats
- :ref:`configuration` — full reference for every parameter in the Excel workbook
- :ref:`excel_workflow` — the CLI commands and step-by-step Excel-driven workflow
- :ref:`programmatic_usage` — driving the pipeline directly from Python

.. toctree::
   :maxdepth: 1
   :hidden:

   data_formats
   configuration
   excel_workflow
   running_from_python